Method and system for rating patents and other intangible assets

ABSTRACT

A statistical patent rating method and system is provided for independently assessing the relative breadth (“B”), defensibility (“D”) and commercial relevance (“R”) of individual patent assets and other intangible intellectual property assets. The invention provides new and valuable information that can be used by patent valuation experts, investment advisors, economists and others to help guide future patent investment decisions, licensing programs, patent appraisals, tax valuations, transfer pricing, economic forecasting and planning, and even mediation and/or settlement of patent litigation lawsuits. In one embodiment the invention provides a statistically-based patent rating method and system whereby relative ratings or rankings are generated using a database of patent information by identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population. For example, a first population of patents having a known relatively high intrinsic value or quality (e.g. successfully litigated patents) is compared to a second population of patents having a known relatively low intrinsic value or quality (e.g. unsuccessfully litigated patents). Based on a statistical comparison of the two populations, certain characteristics are identified as being more prevalent or more pronounced in one population group or the other to a statistically significant degree. Multiple such statistical comparisons are used to construct and optimize a computer model or computer algorithm that can then be used to predict and/or provide statistically-accurate probabilities of a desired value or quality being present or a future event occurring, given the identified characteristics of an individual patent or group of patents.

RELATED APPLICATIONS

[0001] This application claims priority under 35 USC § 120 to U.S. Ser.No. 09/661,765, filed Sep. 14, 2000 (now U.S. Pat. No. 6,556,992), andunder 35 USC § 119(e) to U.S. provisional patent application Ser. No.60/154,066, filed Sep. 14, 1999.

BACKGROUND OF THE INVENTION

[0002] 1. Field of the Invention

[0003] The present invention relates to the field of asset valuationand, in particular, to the field of valuing or rating patents and otherintellectual property assets.

[0004] 2. Description of the Related Art

[0005] Patents play an important role in our economy in encouragingprivate investment in the development of new technologies that improveproductivity and quality of life for everyone. Each year more than aquarter-million patent applications are filed in the United StatesPatent and Trademark Office (“PTO”) resulting annually in the issuanceof over a hundred fifty-thousand patents. Patent owners and applicantspay combined annual fees and costs of nearly a billion dollars (about$6,700 per issued patent) to the PTO to prosecute and maintain theirpatents and applications. This does not include the additional fees andcosts expended for related professional services, such as attorneys feesand drafting charges.

[0006] In addition, each year thousands of patent infringement suits arebrought in the federal courts seeking to enforce patent rights. In the12 months ended Jun. 30, 1992, U.S. federal district courts heard atotal of 1407 such patent cases through trial. See V. Savikas, “SurveyLets Judges Render Some Opinions About the Patent Bar,” Nat'l L. J.,Jan. 18, 1993, at 57. A recent survey conducted by the AmericanIntellectual Property Law Associations (“AIPLA”) reported that themedian cost of patent litigation for each side through trial was about$650,000. AIPLA., “Report of Economic Survey” (1991). Other more recentestimates place the cost of patent enforcement litigation somewhere inthe range of about $1 million per side through trial. Thus, theaggregate annual cost for obtaining, maintaining and enforcing patentsin the United States is easily in the multiple billions of dollars.Similar costs are incurred by patentees in various other foreigncountries where patents may be obtained and enforced.

[0007] Because of the great importance of patents in the both the U.S.and global economies there has been continued interest in quantifyingthe intrinsic value of patents and their contribution to economicprosperity of the individuals or companies that hold and/or controlthem. Such information can be useful for a variety of purposes. Forexample, patent holders themselves may be interested in using suchinformation to help guide future decision-making or for purposes of taxtreatment, transfer pricing or settlement of patent license disputes.Financial advisors and investors may seek to use such information forpurposes of comparative value analysis and/or to construct measures ofthe “fundamental value” of publicly traded companies for purposes ofevaluating possible strategic acquisitions or as a guide to investment.Economists may seek to use patent valuations for purposes of economicforecasting and planning. Insurance carriers may use such valuations toset insurance policy premiums and the like for insuring intangibleassets. See, e.g., U.S. Pat. No. 6,018,714, incorporated herein byreference.

[0008] However, accurate valuing of patents and other intangibleintellectual property assets is a highly difficult task and requires anunderstanding of a broad range of legal, technical and accountingdisciplines. Intellectual property assets are rarely traded in openfinancial markets or sold at auction. They are intangible assets thatsecure unique benefits to the individuals or companies that hold themand/or exploit the underlying products or technology embodying theintellectual property. In the case of patent assets, for example, thisunique value may manifest itself in higher profit margins for patentedproducts, increased market power and/or enhanced image or reputation inthe industry and/or among consumers or investors. These and othercharacteristics of intellectual property assets make such assetsextremely difficult to value.

[0009] Intellectual property valuation specialists have traditionallyemployed three main approaches for valuing patents and other intangibleintellectual property assets. These are: (1) the cost-basis approach;(2) the market approach; and (3) the income approach. See, generally,Smith & Par, Valuation of Intellectual Property and Intangible Assets,2^(nd) Ed. 1989. Each of these traditional accounting-based approachesproduces a different measure or estimate of the intrinsic value of aparticular intellectual property asset in question. The choice of whichapproach is appropriate to use in a given circumstance for a given assetis typically determined by a professional accountant or valuationspecialist, taking into consideration a variety of underlyingassumptions, type of intellectual property asset(s) involved, and howsuch asset(s) are to be employed or exploited. Each of these approachesand the limitations associated therewith are briefly discussed below.

[0010] Cost Basis Approach

[0011] The first and simplest approach is the so-called cost-basisapproach. This approach is often used for tax appraisal purposes or forsimple “book value” calculations of a company's net assets. Underlyingthis valuation method is the basic assumption that intellectual propertyassets, on average, have a value roughly equal to their cost-basis. Thesupporting rationale is that individuals and companies invest inintellectual property asset(s) only when the anticipated economicbenefits of the rights to be secured by the intellectual propertyasset(s) exceed the anticipated costs required to obtain the asset(s),taking into account appropriate risk factors, anticipated rates ofreturn, etc. In theory, a rational economic decision-maker would notinvest in a patent or other intellectual property asset if he or she didnot believe that it would produce expected economic benefits (tangibleor otherwise) at least equal to its anticipated cost-basis.

[0012] There are several drawbacks or limitations associated with thecost-basis valuation approach which limit its general applicability. Onesignificant drawback is that the approach assumes a rational economicdecision-maker. While such assumption might be statistically valid on amacro scale where many individual decisions and decision-makers areimplicated (e.g., valuing all patents or a large subset of all patents),it is not necessarily a valid assumption when conducting valuationanalysis on a micro scale (e.g., valuing a single patent or a portfolioof patents). It is one thing to assume that, on average, individualinvestment decisions and decision-makers are rational and economicallymotivated. It is a wholly different thing to assume that “each”investment decision or decision-maker is rational and economicallymotivated.

[0013] For a variety of reasons certain individuals or companies mayinvest uneconomically in patents or other intellectual propertyassets—for example, to achieve personal recognition or to superficially“dress up” balance sheets to attract potential investors or buyers. Avariety of individual psychological factors may also influenceinvestment decisions producing sometimes irrational or non-economicaldecisions. For example, the so-called “lottery effect” may encouragesome individuals or companies to over-invest in highly speculativetechnologies that have the seductive allure of potentially huge economicrewards, but very little if any probability of success. Yet others mayinvest uneconomically in patents and/or other intellectual propertyassets because of fundamental misunderstandings or misinformationconcerning the role of intellectual property and how it can berealistically and effectively exploited.

[0014] But even assuming a well-informed, rational,economically-motivated decision-maker, the cost-basis approach is stillsusceptible to inherent uncertainties in the decision-maker's informedand honest projections of the anticipated economic benefits to be gainedby a patent or other intellectual property asset. These benefits areoften unknown even to the patentee until well after the patent has beenapplied for and often not until long after the patent has issued. Manynew inventions that may look promising on paper or in the laboratoryturn out to be economically or commercially infeasible for a variety ofreasons and, as a result, patents covering such inventions may havelittle if any ultimate intrinsic economic value. Other inventions thatmay seem only marginal at the time the patent is applied for may turnout to be extremely valuable and, if a broad scope of protection isobtained, may return economic benefits far in excess of the cost-basisof the patent. The cost basis approach thus fails to differentiatebetween these two extremes because (all other things being equal) thecost basis is the same for securing a patent on the worthless inventionas it is for securing a patent on the valuable invention.

[0015] The cost-basis approach also does not account for the possibilityof evolution of products and technology over time and changing businessand economic conditions. Rather, the cost-basis approach implicitlyassumes a static business and economic environment, providing a fixedvalue based on actual costs expended at the time of the initialinvestment without taking into account how the value of that investmentmight change over time. As a result of these and other short-comings,the cost-basis approach has only limited utility as a method foraccurately estimating the intrinsic economic value of patents or otherintellectual property assets in real-world business environments.

[0016] Market Approach

[0017] The second traditional valuation approach—the marketapproach—seeks to provide real-world indications of value by studyingtransactions of similar assets occurring in free and open markets. Intheory, the market approach can provide very accurate measures orestimates of intrinsic value. In practice, however, there are very fewopen financial markets that support active trading of intellectualproperty and other similar intangible assets. Most intellectual propertyassets are bought or sold in private transactions involving sales ofentire businesses or portions of businesses. And even if the financialparticulars of each such transaction were readily available, it would bedifficult, if not impossible, to disaggregate the intellectual propertyassets involved in the transaction from the other assets and allocate anappropriate value to them.

[0018] As a result of these and other practical difficulties, there ispresently very little direct real-World data on which to base marketcomparisons of intellectual property and other similar intangibleassets. Nevertheless, several interesting studies have been reportedwhich attempt indirectly to extract market-based valuations of patentsand other intellectual property assets by studying the stock prices ofvarious publicly traded companies that hold such assets. See, Hall,“Innovation and Market Value,” Working Paper No. 6984 NBER (1999); andCockburn et al., “Industry Effects and Appropriability Measures in theStock Market's Valuation of R&D and Patents,” Working Paper No. 2465NBER (1987).

[0019] While interesting in their approach, the usefulness of themethodologies taught by these studies in terms of valuing individualpatent and other intellectual property assets is limited. Such indirectmarket-based valuation approaches mostly attempt to establish only ageneralized correlation between stock prices of publicly tradedcompanies and the aggregate number of intellectual property assets heldor controlled by those companies. Because individual stock prices aregenerally reflective of the overall aggregated assets of a company andits future earnings potential, such indirect market-valuation approachesdo not lend themselves readily to valuing individual identifiedintellectual property assets. Moreover, intellectual property and otherintangible assets owned by publicly traded companies comprise only afraction of the total population of potential intellectual propertyassets that may be of interest.

[0020] A computer-automated variation of the traditional market approachspecifically adapted for rating patent portfolios is described in U.S.Pat. No. 5,999,907. In this case, a first database is providedcontaining information describing selected characteristics of aportfolio of patents to be acquired. A second database is providedcontaining empirical data describing selected characteristics ofrepresentative patent portfolios having known market values. Estimatedvaluations are obtained by comparing information in the first data baseto information in the second database to determine which known patentportfolio the portfolio to be acquired matches the closest. The value ofthe closest matching known portfolio is then used as a roughapproximation of the value of the portfolio to be acquired.

[0021] While such approach provides an innovative variation of themarket-based valuation technique described above, it is again ultimatelylimited by the need to acquire relevant market data of known patentportfolios. As noted above, such information is very difficult toobtain. Unless a large amount of such data could be collected andanalyzed, the effectiveness and accuracy of such an approach would bevery limited. Even if a large amount of such data could be collected andstored in a suitable computer-accessible database, the process ofindividually retrieving and comparing relevant characteristics of eachrepresentative portfolio in the database would be undesirably timeconsuming, even using a high-speed computer. Moreover, the statisticalaccuracy of the resulting approximated valuations would be undetermined.

[0022] Income Approach

[0023] The third and perhaps most commonly used accounting-basedapproach for valuing intellectual property and other intangible assetsis the so-called income approach. This approach can provide accurate andcredible valuations of intellectual property assets in certainsituations where an isolated stream (or streams) of economic benefit canbe identified and attributed to an intellectual property asset inquestion. The income approach values an intellectual property asset bycapitalizing or discounting to present value all future projectedrevenue streams likely to be derived from its continued exploitation.For example, if a patent asset is licensed under an agreement thatprovides for a predictable income stream over a certain period of timeinto the future, then the intrinsic value of the patent may beaccurately calculated by taking the net discounted present value of theresidual income stream (less any scheduled maintenance costs).Similarly, if the patentee is directly exploiting the patent itself, theintrinsic value of the patent may be calculated by taking the netdiscounted value of the incremental profit stream (assuming it can beidentified) attributable to the patent over the remaining life of thepatent or the economic life of the patented technology.

[0024] In theory, the income valuation approach can produce veryaccurate estimates of intrinsic value for certain intellectual propertyand other intangible assets. In practice, however, it is often difficultto identify with certainty and precision an isolated income streamattributable to a particular intellectual property asset in question,let alone an income stream that is predictable over time. In addition,many intellectual property assets, particularly newly issued patents,are not licensed or exploited at all and, therefore, there are noidentifiable income streams upon which to base a valuation.

[0025] In such circumstances many asset valuation specialists attempt toproject possible or hypothetical future revenue streams or economicbenefits based on available data of other similar companies in theindustry and/or other license agreements for similar intellectualproperty assets in the same general technical field. Some patentvaluation experts have even established extensive data-bases of patentlicenses and have attempted to establish a schedule of “standard” orbaseline royalty rates or royalty ranges for patent licenses in variousindustries for purposes of forecasting possible future revenue streams.While such information can be very helpful, without an actualdemonstrated income stream or other proven economic benefit, theincome-based valuation approach loses credibility and can become morespeculation than valuation.

[0026] Each of the above valuation approaches has its characteristicstrengths and weaknesses. Of course, no single valuation method canprovide absolute certainty of the true intrinsic value of an asset. Thisis especially true when valuing patents and other intangibleintellectual property assets. Nevertheless, a need exists for acomparative valuation technique that overcomes the aforementionedproblems and limitations and which does not require collectingcomparative market data of existing patent portfolios or calculatingfuture hypothetical income streams or royalty rates. There is a furtherneed for an intellectual property valuation method that producesstatistically accurate valuations, ratings or rankings according to adetermined statistical accuracy.

SUMMARY OF THE INVENTION

[0027] The present invention compliments and improves upon traditionalvaluation approaches by providing an objective, statistical-based ratingmethod and system for independently assessing the relative breadth(“B”), defensibility (“D”) and commercial relevance (“R”) of individualpatent assets and other intangible intellectual property assetsaccording to a determined statistical accuracy. Thus, the invention canbe used to provide new and valuable information that can be used bypatent valuation experts, investment advisors, economists and others tohelp guide future patent investment decisions, licensing programs,patent appraisals, tax valuations, transfer pricing, economicforecasting and planning, and even mediation and/or settlement of patentlitigation lawsuits.

[0028] In one embodiment the invention provides a statistically-basedpatent rating method and system whereby relative ratings or rankings aregenerated using a database of patent information by identifying andcomparing various characteristics of each individual patent to astatistically determined distribution of the same characteristics withina given patent population. For example, a first population of patentshaving a known relatively high intrinsic value or quality (e.g.successfully litigated patents) is compared to a second population ofpatents having a known relatively low intrinsic value or quality (e.g.unsuccessfully litigated patents). Based on a statistical comparison ofthe two populations, certain characteristics are identified as beingmore prevalent or more pronounced in one population group or the otherto a statistically significant degree. Multiple such statisticalcomparisons are used to construct and optimize a computer model orcomputer algorithm that can then be used to accurately predict and/orprovide statistically-accurate probabilities of a desired value orquality being present or a future event occurring, given the identifiedcharacteristics of an individual patent or group of patents.

[0029] The algorithm may comprise a simple scoring and weighting systemwhich assigns scores and relative weightings to individual identifiedcharacteristics of a patent or group of patents determined to havestatistical significance. For example, positive scores would generallybe applied to those patent characteristics having desirable influenceand negative scores would apply to those patent characteristics havingundesirable influence on the particular quality or event of interest. Ahigh-speed computer is then used to repeatedly test the algorithmagainst one or more known patent populations (e.g., patents declared tobe valid/invalid or infringed/non-infringed). During and/or followingeach such test the algorithm is refined by adjusting the scorings and/orweightings until the predictive accuracy of the algorithm is optimized.Once the algorithm is suitably optimized, selected metrics for anindividual identified patent or group of patents to be rated are inputinto the algorithm and the algorithm is operated to calculate anestimated rating or mathematical score for that patent or group ofpatents. Individual results could be reported as statisticalprobabilities of a desired quality being present, or a future eventoccurring (patent being litigated, abandoned, reissued, etc.) over aspecified period in the future. Results could also be provided asarbitrary raw scores representing the sum of an individual patent'sweighted scores, which raw scores can be further ranked and reported ona percentile basis within a given patent population and/or upon anyother comparative or non-comparative basis as desired.

[0030] The first and second patent populations selected for analysis arepreferably roughly the same size and may comprise essentially any twogroups of patents (or identifiable subsets of a single group of patents)having different actual or assumed intrinsic values or other qualitiesof interest. For example, the first population may consist of a randomsample of 500-1000 patents that have been successfully litigated (foundvalid and infringed) and the second population may consist of a randomsample of 500-1000 patents that have been unsuccessfully litigated(found either invalid or not infringed). Alternatively, the firstpopulation may consist of a random sample of patents that have beenlitigated and found valid regardless of whether infringement is alsofound, and the second population may consist of a random sample ofpatents that have been found invalid. Likewise, the first population mayconsist of a random sample of patents that have been litigated and foundinfringed regardless of the validity finding and the second populationmay consist of a random sample of patents that have been found notinfringed.

[0031] The selection of which study population(s) to use depends uponthe focus of the statistical inquiry and the desired quality (e.g.,claim scope, validity, enforceability, etc.) of the patent asset desiredto be elicited. For example, if validity is the quality of interest,then the first and second patent populations may preferably be selectedsuch that one population is known or predicted to have a higherincidence of invalid patents than the other population. This informationmay be readily gathered from published patent decisions of the FederalCircuit and/or the various federal district courts. Thus, the firstpopulation may consist of a random sample of patents declared invalid bya federal court and the second population may consist of a random sampleof patents from the general patent population, which are presumed to bevalid. Alternatively, the second population may consist of a randomsample of patents declared “not invalid” by a federal court following avalidity challenge.

[0032] The approach is not limited, however, to analyzing litigatedpatents. For example, fruitful comparisons may also be made betweenlitigated patents (presumably the most valuable patents) andnon-litigated patents; or between high-royalty-bearing patents andlow-royalty-bearing patents; or between high-cost-basis patents andlow-cost-basis patents; or between published patent applications andissued patents. The number and variety of definable patent populationshaving different desired qualities or characteristics capable offruitful comparison in accordance with the invention herein is virtuallyunlimited. While not specifically discussed herein, those skilled in theart will also recognize that a similar approach may also be used forvaluing and/or rating other intellectual property or intangible assetssuch as trademarks, copyrights, domain names, web sites, and the like.

[0033] In accordance with another embodiment the invention provides amethod for rating or ranking patents. In accordance with the method, afirst population of patents is selected having a first quality orcharacteristic and a second population of patents is selected having asecond quality or characteristic that is different from the firstquality or characteristic. Statistical analysis is performed todetermine or identify one or more patent metrics having either apositive or negative correlation with either said first or secondquality to a statistically significant degree. A regression model isconstructed using the identified patent metric(s). The regression modelis iteratively adjusted to be generally predictive of either the firstor second quality being present in a given patent. The regression modelis used to automatically rate or rank patents by positively weighting orscoring patents having the positively correlated patent metrics andnegatively weighting or scoring patents having the negatively correlatedpatent metrics (“positive” and “negative” being used here in therelative sense only). If desired, the method may be used to generate apatent rating report including basic information identifying aparticular reported patent or patents of interest and one or moreratings or rankings determined in accordance with the method describedabove.

[0034] In accordance with another embodiment the invention provides astatistical method for scoring or rating selected qualities ofindividual patents and for generating a rating report specific to eachindividual patent rated. The method begins by providing a first databaseof selected patent information identifying and/or quantifying certainselected characteristics of individual patents from a first populationof patents having a selected patent quality of interest. A seconddatabase (or identified subset of the first database) of selected patentinformation is also provided identifying and/or quantifying certainselected characteristics of individual patents from a second populationof patents generally lacking or having reduced incidence of the selectedpatent quality of interest. Statistical analysis is performed toidentify one or more characteristics that are statistically moreprevalent or more pronounced in either the first or second patentpopulation to a statistically significant degree. Based on thisinformation and the identified characteristics, individual patents maybe scored or rated by positively weighting those having the same orsimilar characteristics and negatively weighting those lacking the sameor similar characteristics. If desired, the method may be used togenerate a patent rating report including basic information identifyinga particular reported patent or patents of interest and one or moreratings or rankings determined in accordance with the method describedabove.

[0035] In accordance with another embodiment the invention provides amethod and automated system for rating or ranking patents or otherintangible assets. In accordance with the method a first population ofpatents is selected having a first quality or characteristic and asecond population of patents is selected having a second quality orcharacteristic that is different from or believed to be different fromthe first quality or characteristic. A computer accessible database isprovided and is programmed to contain selected patent metricsrepresentative of or describing particular corresponding characteristicsobserved for each patent in the first and second patent populations. Acomputer regression model is constructed and adjusted based on theselected patent metrics. The regression model is operable to input theselected patent metrics for each patent in the first and second patentpopulations and to output a corresponding rating or ranking that isgenerally predictive of the first and/or second quality being present ineach patent in the first and second patent populations. The regressionmodel may then be used to rate or rank one or more patents in a thirdpatent population by inputting into the regression model selected patentmetrics representative of or describing corresponding characteristics ofone or more patents in the third population.

[0036] In accordance with another embodiment the invention provides ahigh-speed method for automatically scoring or rating a sequentialseries of newly issued patents as periodically published by the PTO andfor determining and storing certain rating or scoring informationspecific to each patent. According to the method, a substantial fulltext copy of each patent in the sequential series is obtained in acomputer text file format or similar computer-accessible format. Acomputer program is caused to automatically access and read eachcomputer text file and to extract therefrom certain selected patentmetrics representative of or describing particular observedcharacteristics or metrics of each patent in the sequential series. Theextracted patent metrics are input into a computer algorithm. Thealgorithm is selected and adjusted to produce a corresponding ratingoutput or mathematical score that is generally predictive of aparticular patent quality of interest and/or the probability of aparticular future event occurring. Preferably, for each patent in thesequential series the rating output or mathematical score is stored in acomputer accessible storage device in association with other selectedinformation identifying each rated patent such that the correspondingrating or score may be readily retrieved for each patent in thesequential series.

[0037] In accordance with another embodiment the invention provides amethod for valuing individual selected patents. A patent valuedistribution curve and/or data representative thereof is provided. Theshape of the curve generally represents an estimated distribution ofpatent value according to percentile rankings within a predeterminedpatent population. The area under the curve is generally proportional tothe total approximated value of all patents in the predetermined patentpopulation. Individual selected patents from the population are rankedin accordance with selected patent metrics to determine an overallpatent quality rating and ranking for each individual selected patent.The patent value distribution curve is then used to determine acorresponding estimated value for an individual selected patent inaccordance with its overall patent quality ranking. If desired, themethod may be used to generate a patent valuation report including basicinformation identifying a particular reported patent or patents ofinterest and one or more valuations determined in accordance with themethod described above.

[0038] In accordance with another embodiment, the invention provides anautomated method for scoring or rating patents in accordance withuser-defined patent metrics and/or patent populations. The automatedmethod is initiated by a user selecting a patent, or group of patents,to be rated. A full-text computer accessible file of the patent to berated is retrieved from a central database, such as that currentlymaintained by the U.S. Patent & Trademark Office at www.uspto.gov. Acomputer algorithm evaluates the full-text file of the patent to berated and extracts certain selected patent metric(s), which may bepredefined, user-defined, or both. Based on the selected patentmetric(s), the algorithm computes a rating number or probability (e.g.,between 0 and 1) corresponding to the likely presence or absence of oneor more user-defined qualities of interest in the patent to be ratedand/or the probability of one or more possible future events occurringrelative to the patent. If desired, the rating number or probability canbe further ranked against other similar ratings for patents within aselected patent population, which may be predetermined, user-defined, orboth. Thus, the method in accordance with the preferred embodiment ofthe invention is capable of producing multiple independent ratingsand/or rankings for a desired patent to be rated, each tailored to adifferent user-defined inquiry, such as likelihood of the patent beinglitigated in the future, being held invalid, likelihood of successfulinfringement litigation, predicted life span of the patent, relativevalue of the patent, etc.

[0039] For purposes of summarizing the invention and the advantagesachieved over the prior art, certain objects and advantages of theinvention have been described herein above. Of course, it is to beunderstood that not necessarily all such objects or advantages may beachieved in accordance with any particular embodiment of the invention.Thus, for example, those skilled in the art will recognize that theinvention may be embodied or carried out in a manner that achieves oroptimizes one advantage or group of advantages as taught herein withoutnecessarily achieving other objects or advantages as may be taught orsuggested herein.

[0040] All of these embodiments and obvious variations thereof areintended to be within the scope of the invention herein disclosed. Theseand other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription of the preferred embodiments having reference to theattached figures, the invention not being limited to any particularpreferred embodiment(s) disclosed.

BRIEF DESCRIPTION OF THE FIGURES

[0041] Having thus summarized the overall general nature of theinvention and its features and advantages, certain preferred embodimentsand examples will now be described in detail having reference to thefigures that follow, of which:

[0042]FIG. 1 is a simplified schematic system block-diagram illustratingone possible embodiment of a patent rating method and system havingfeatures and advantages in accordance with the present invention;

[0043]FIG. 2 is a simplified schematic flow chart of one possiblemultiple regression technique suitable for carrying out the ratingmethod and system of FIG. 1;

[0044]FIG. 3 is a graph of percentages of litigated patents according toage, illustrating the declining incidence of patent litigation withpatent age;

[0045] -FIG. 4 is a graph of percentages of litigated patents found tobe infringed by a federal district court according to the average numberof words per independent claim, illustrating the declining incidence ofpatent infringement with length of patent claim;

[0046]FIG. 5 is a graph of litigated patents according to technicalfield, illustrating the incidence of patent infringement holdings byfield;

[0047]FIG. 6 is a graph of litigated patents according to technicalfield, illustrating the incidence of patent invalidity holdings byfield;

[0048]FIG. 7 is a graph of percentages of litigated patents found to beinvalid by a federal district court according to the average age ofcited U.S. patent references, illustrating the declining incidence ofpatent invalidity with citation age;

[0049]FIG. 8 is a graph of overall patent maintenance rates for patentsin the general patent population, illustrating increasing rates ofpatent mortality with age;

[0050]FIG. 9 is a graph of patent mortality rates for patents havingdifferent numbers of claims, illustrating decreasing mortality rateswith increasing number of claims;

[0051]FIG. 10 is a graph of patent mortality rates for patents havingdifferent numbers of figures, illustrating decreasing mortality rateswith increasing number of figures;

[0052]FIG. 1 is one possible preferred embodiment of a patent ratingreport generated in accordance with the method and system of FIG. 1 andhaving features and advantages of the present invention; and

[0053]FIG. 12 is one possible example of a patent value distributioncurve for use in accordance with one embodiment of a patent valuationmethod of present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0054] The utility of the present invention begins with the fundamentalobservation that not all intellectual property assets are created equal.In the case of patent assets, for example, two patents even in the sameindustry and relating to the same subject matter can command drasticallydifferent royalty rates in a free market, depending upon a variety offactors. These factors may include, for example: (1) the premium orincremental cost consumers are willing to pay for products or servicesembodying the patented technology; (2) the economic life of the patentedtechnology and/or products; (3) the cost and availability of competingsubstitute technology and/or products; and (4) the quality of theunderlying patent asset.

[0055] The quality of a patent in terms of the breadth or scope ofrights secured, its defensibility against validity challenges and itscommercial relevance can have particularly dramatic impact on its value.Obviously, a patent that has a very narrow scope of protection or thatis indefensible against a validity challenge will have much less valuethan a patent that has a broad scope of protection and strongdefensibility. A skilled patent lawyer can examine the claims andspecification of a patent, its prosecution history and cited prior artand, based on a detailed legal analysis, render a subjective opinion asto the likely scope and defensibility of the patent. However, such legalwork is time-intensive and expensive. Thus, it may not be economicallyfeasible to consult with a patent lawyer in every situation where suchinformation may be desired.

[0056] The patent rating method and system of the present invention isnot proposed to replace conventional legal analysis or traditionalvaluation methods, but to complement and support the overall evaluativeprocess. In one embodiment, the present invention provides an objective,statistical-based rating method and system for substantiallyindependently assessing the relative breadth (“B”), defensibility (“D”)and commercial relevance (“R”) of individual patent assets and otherintangible intellectual property assets. Thus, the invention can providenew and valuable information which can be used by patent valuationexperts, investment advisors, economists and others to help guide futurepatent investment decisions, licensing programs, patent appraisals, taxvaluations, transfer pricing, economic forecasting and planning, andeven mediation and/or settlement of patent litigation lawsuits. Suchinformation may include, for example and without limitation:statistically calculated probabilities of particular desired orundesired qualities being present; statistical probabilities of certainfuture events occurring relative to the asset in question; ratings orrankings of individual patents or patent portfolios; ratings or rankingsof patent portfolios held by public corporations; ratings or rankings ofpatent portfolios held by pre-IPO companies; ratings or rankings ofindividual named inventors; and ratings or rankings of professionalservice firms, law firms and the like who prepare, prosecute and enforcepatents or other intellectual property assets.

[0057] In its simplest form the present invention provides a statisticalpatent rating method and system for rating or ranking patents based oncertain selected patent characteristics or “patent metrics.” Such patentmetrics may include any number of quantifiable parameters that directlyor indirectly measure or report a quality or characteristic of a patent.Direct patent metrics measure or report those characteristics of apatent that are revealed by the patent itself, including its basicdisclosure, drawings and claims, as well as the PTO record or filehistory relating to the patent. Specific patent metrics may include, forexample and without limitation, the number of claims, number of wordsper claim, number of different words per claim, word density (e.g.,different-words/total-words), length of patent specification, number ofdrawings or figures, number of cited prior art references, age of citedprior art references, number of subsequent citations received, subjectmatter classification and sub-classification, origin of the patent(foreign vs. domestic), payment of maintenance fees, prosecutingattorney or firm, patent examiner, examination art group, length ofpendency in the PTO, claim type (i.e. method, apparatus, system), etc.

[0058] Indirect patent metrics measure or report a quality orcharacteristic of a patent that, while perhaps not directly revealed bythe patent itself or the PTO records relating to the patent, can bedetermined or derived from such information (and/or other informationsources) using a variety of algorithms or statistical methods including,but not limited to, the methods disclosed herein. Examples of indirectpatent metrics include reported patent litigation results, publishedcase opinions, patent licenses, marking of patented products, and thelike. Indirect patent metrics may also include derived measures ormeasurement components such as frequency or infrequency of certain wordusage relative to the general patent population or relative to a definedsub-population of patents in the same general field.

[0059] For example, each word and/or word phrase in a patent claim(and/or patent specification) could be assigned a point value accordingto its frequency of use in a randomly selected population of similarpatents in the same general field. Statistically common words or wordphrases such as simple articles, pronouns and the like could receiverelatively low point values. Uncommon words or word phrases couldreceive relatively high point values. The total point score for eachclaim could then be taken as an indication of its relative breadth ornarrowness based on the total number and statistical prevalence of eachof the words contained in the claim. Optionally, different amounts ofpoints can be accorded to claim words or word phrases based on whetheror not they also appear in the patent specification. Multiple claimsand/or patents could also be combined into a single analysis, ifdesired.

[0060] In accordance with one preferred embodiment of the inventionrelative ratings or rankings are generated using a database of selectedpatent information by identifying and comparing various relevantcharacteristics or metrics of individual patents contained in thedatabase. In one example, a first population of patents having a knownor assumed relatively high intrinsic value (e.g. successfully litigatedpatents) are compared to a second population of patents having a knownor assumed relatively low intrinsic value (e.g. unsuccessfully litigatedpatents). Based on the comparison, certain characteristics areidentified as statistically more prevalent or more pronounced in onepopulation group or the other to a significant degree.

[0061] These statistical comparisons are then used to construct andoptimize a computer model or computer algorithm comprising a series ofoperative rules and/or mathematical equations. The algorithm is used topredict and/or provide statistically determined probabilities of adesired value or quality being present and/or of a future eventoccurring, given the identified characteristics of an individualidentified patent or group of patents. The algorithm may comprise asimple scoring and weighting system which assigns scores and relativeweightings to individual identified characteristics of a patent or groupof patents determined (or assumed) to have statistical significance. Forexample, positive scores could generally be applied to those patentcharacteristics determined or believed to have desirable influence andnegative scores could be applied to those patent characteristicsdetermined or assumed to have undesirable influence on the particularquality or event of interest.

[0062] Once the basic algorithm is constructed, a high-speed computer ispreferably used to repeatedly test the algorithm against one or moreknown patent populations (e.g. patents declared to be valid/invalid orinfringed/non-infringed). During and/or following each such test thealgorithm is refined (preferably automatically) by iteratively adjustingthe scorings and/or weightings assigned until the predictive accuracy ofthe algorithm is optimized. Adjustments can be made automatically in anorderly convergence progression, and/or they can by made randomly orsemi-randomly. The latter method is particularly preferred where thereare any non-linearities in the equations or rules governing thealgorithm. Algorithm results are preferably reported as statisticalprobabilities of a desired quality being present, or a future eventoccurring (e.g., patent being litigated, abandoned, reissued, etc.)during a specified period in the future. Algorithm results could also beprovided as arbitrary raw scores representing the sum of an individualpatent's weighted scores, which raw scores can be further ranked andreported on a percentile basis or other similar basis as desired.Preferably, the statistical accuracy of the algorithm is tracked andreported over time and periodic refinements are made as more and moredata is collected and analyzed.

[0063] System Architecture

[0064]FIG. 1 is a simplified block diagram of one possible embodiment ofa patent rating method and automated system 100 having features andadvantages in accordance with the present invention. The system isinitiated at the START block 110. At block 120 certain characteristicsC_(a) of Patent Population “A” are inputted from a database 125 in theform:

Ca={A ₁ , A ₂ . . . An}

[0065] where: C_(a)=set of selected characteristics of Pat. Pop. “A”

[0066]  A_(n)=an individual selected characteristic of Pat. Pop. “A”

[0067] At block 130 characteristics Cb of Patent Population “B” areinputted from a database 135 in the form:

C _(b) ={B ₁ , B ₂ . . . B _(n) }

[0068] where: C_(b)=set of selected characteristics of Pat. Pop. “B”

[0069]  B_(n)=an individual selected characteristic of Pat. Pop. “B”

[0070] Preferably, Patent Population “A” and Patent Population “B” areselected to have different known or assumed intrinsic values and/orqualities such that a fruitful comparison may be made. For example,Population “A” may comprise a random or semi-random (e.g.,representative) sample of successfully litigated patents and/orindividual patent claims. Population “B” may comprise a random orsemi-random sample of unsuccessfully litigated patents and/or individualpatent claims. In that case, Population “A” patents/claims may beassumed to have higher intrinsic value than Population “B”patents/claims. Alternatively, and regardless of whatever assumed orintrinsic economic value the patents may have, Population “A” patentsmay be described as having the quality of being successfully litigated(infringement or validity), whilst Population “B” patents may bedescribed as having the quality of being unsuccessfully litigated(infringement or validity). Thus, by examining and comparing thecharacteristics of litigated patents/claims that fall into eitherpopulation “A” or “B”, one can make certain statistical conclusions andpredictions about other patents that may or may not have been litigated.Such probabilistic analysis can also be easily extended to accuratelycalculate the odds, for example, of prevailing on a particular patentinfringement claim or defense in a particular litigation proceeding(e.g., preliminary injunction motion, summary judgment motion, jurytrial, bench trial, appeal, etc.). Such information would be oftremendous value to patent litigants, for example.

[0071] Of course, the study populations are not limited to litigatedpatents/claims. For example, one study population may comprise a randomor semi-random sample of patents selected from the general patentpopulation and having a representative “average” value or quality. Theother study population may comprise, for example and without limitation,a random or semi-random sample of patents selected from a sub-populationconsisting of all patents for which 1^(st), 2^(nd) or 3^(rd) maintenancefees have been paid; or all patents that have been licensed for morethan a predetermined royalty rate; or all patents that have beensuccessfully reissued/reexamined; or all patents that have relatedcounterpart foreign patents; or all patents that have been subsequentlycited by other patents at least X times; etc. The number and variety ofpossible ways to define study populations of interest in accordance withthe invention are virtually limitless.

[0072] Next, at block 140 a comparison is made between the selectedcharacteristics C_(a) of Patent Population “A” and the same selectedcharacteristics C_(b) of Patent Population “B”. Based on the comparison,certain characteristics are identified at block 144 as beingstatistically more prevalent or more pronounced in one population or theother to a significant degree. This comparison can be performed and thestatistical significance of observed differences determined by applyingknown statistical techniques. Thus, certain statistically relevantcharacteristics of each study population can be readily identified anddescribed mathematically and/or probabilistically.

[0073] At block 148 a multiple regression model is constructed using theidentified statistically relevant characteristics determined at block144. Multiple regression modeling is a well-known statistical techniquefor examining the relationship between two or more predictor variables(PVs) and a criterion variable (CV). In the case of the presentinvention the predictor variables (or independent variables) describe orquantify the selected relevant characteristics of a particular patentpopulation, e.g., class/sub-class, number of independent claims, numberof patent citations, length of specification, etc. Criterion variables(or dependent variables) measure a selected quality of a particularpatent population, such as likelihood of successful litigation (eithervalidity or infringement). Multiple regression modeling allows thecriterion variable to be studied as a function of the predictorvariables in order to determine a relationship between selectedvariables. This data, in turn, can be used to predict the presence orabsence of the selected quality in other patents. The regression modelhas the form:

CV _(m) =ƒ{PV ₁ , PV ₂ . . . PV _(n)}

[0074] where: CV_(m)=criterion variable (e.g., quality desired to bepredicted)

[0075]  PV_(n)=predictor variable (e.g., statistically relevantcharacteristic)

[0076] Once the regression model is completed it can be applied at block150 to predict the presence or absence of the selected quality in otherpatents selected from Patent Population “C”, for example, which may bethe same as or different from Populations “A” or “B.” CharacteristicsC_(c) of each individual patent P_(n) to be analyzed are inputted atblock 150 from a database 155 in the form:

C _(C) ={C ₁ , C ₂ . . . C _(n)}

[0077] where: C_(c)=set of selected characteristics of a patent P_(n)

[0078]  C_(n)=an individual selected characteristic of patent P_(n)

[0079] The relevant characteristics PV_(n) of patent P_(n) areidentified and plugged into the regression model at block 160. Theresulting predicted value or score CV_(m), representing the quality ofinterest for patent P_(n), is then outputted to a data output file 178,printer or other output device, as desired. The system terminates atSTOP block 180.

[0080] Statistical Methodology

[0081] Many different methods of statistical analysis may be suitablyemployed to practice the present invention. The preferred methodology isa multiple regression technique performed, for example, by a high-speedcomputer. As noted above, multiple regression modeling is a statisticaltechnique for examining the relationship between two or more predictorvariables (PVs) and a criterion variable (CV). In the case of thepresent invention the predictor variables (or independent variables)describe or quantify certain observable characteristics of a particularpatent population, e.g., number of independent claims, length ofspecification, etc. Criterion variables (or dependent variables) measurea selected quality of interest of a particular patent population, suchas likelihood of successful litigation, validity or infringement.Multiple regression modeling allows the criterion variable to be studiedas a function of the predictor variables in order to determine arelationship between selected variables. This data, in turn, can be usedto predict the presence or absence of the selected quality in otherpatents.

[0082] For example, if one were interested in examining the relationshipbetween the number of times the word “means” is used in a claim (the PV)and a finding of infringement in litigation (the CV), one could use thefollowing simple linear regression model:

Y=a+bXi

[0083] Where:

[0084] Y=criterion variable (likelihood of patent infringement)

[0085] Xi=predictor variable (number of times “means” appears)

[0086] a=the Y-intercept (% found infringed where Xi=0)

[0087] b=the rate of change in Y given one unit change in Xi

[0088] The coefficients a, b can be determined by iteration or othermeans so that the sum of squared errors is minimized in accordance withthe well-known ordinary least squares (OLS) technique. Given leastsquares fit, the mean of the errors will be zero.

[0089] The above example is a single-variable, linear regression model.In carrying out the present invention, those skilled in the art willreadily appreciate that it may be desirable to include a number ofdifferent predictor variables (PVs) in the regression model (expressedeither as linear or non-linear functions and/or rules) in order toextract useful information from available patent data. FIG. 2 is asimplified schematic flow chart 200 of one such suitable multipleregression technique that may be employed in carrying out the presentinvention.

[0090] The flow chart begins at the START block 202. At block 204certain system variables are initialized. These include multi-regressioncoefficients a, b, c and d, incremental step changes Δa, Δb, Δc and Δdfor each coefficient a, b, c and d, respectively, and various countersCO (#correct predictions), IN (# incorrect predictions), n (# patent inpopulation) and m (loop repeat count). At step 206 the system inputsselected characteristics (C_(n)=X₁, X₂, X₃) of the next patent (n) inthe study population (e.g., litigated patents). Preferably, thecharacteristics X₁, X₂, X₃ have been previously selected and determinedto have a statistically significant impact on the selected patentquality desired to be measured. At step 208 the observed patent qualityY of patent n is inputted into the system. In this case, the patentquality of interest is the validity or invalidity of the patent asdetermined by a final judgment of a court. Alternatively, the measuredpatent quality could be any one or more of a number of other qualitiesof interest such as discussed above.

[0091] At step 210 the system calculates a predicted patent quality suchas the probability that the patent in question is valid P(valid). Inthis case, a simple linear multi-regression model is chosen having theform:

P(valid)=a+bX ₁+cX₂+dX₃

[0092] where:

[0093] P(valid)=predicted probability of patent validity

[0094] X₁, X₂, X₃ are various predictor variables

[0095] a=Y-intercept (% found valid where X₁, X₂, X₃=0)

[0096] b,c,d=rate of change in P(valid) per unit change of X₁, X₂, X₃

[0097] Once the probability of validity is calculated, the system atstep 212 determines an expected quality Y′ based on the probabilityP(valid). In particular, if P(valid) is calculated to be greater than0.5 (>50%) then the expected outcome Y′ is that the patent is “VALID” asindicated by block 214. If P(valid) is calculated to be less than 0.5(<50%) then the expected outcome Y′ is that the patent is “INVALID” asindicated by block 216.

[0098] The expected patent quality or outcome Y′ is then compared to theactual observed patent quality Y at step 220 and a determination is madewhether Y=Y′ indicating a correct prediction (block 218) or whether Y< >Y′ indicating an incorrect prediction (block 222). In the case of acorrect prediction the counter CO is incremented. In the event of anincorrect prediction, the counter IN is incremented. If patent(n) is notthe last patent in the study population, then decision bock 226 directsthe system to loop back again repeating the above steps 206-226 for thenext patent n=n+1 in the population and incrementing the patent countern at block 224. If patent(n) is the last patent in the population(n=#pop) then decision block 226 directs the system to begin astatistical analysis of the regression model.

[0099] This analysis begins at block 228 wherein the statisticalaccuracy (SA) of the model (m) is calculated using the equation:

SA(m)=CO/(CO+IN)

[0100] where:

[0101] SA(m)=statistical accuracy of regression model (m)

[0102] CO=number of correct predictions for model (m)

[0103] IN=number of incorrect predictions for model (m)

[0104] The statistical accuracy SA(m) is a simple and easily calculatedmeasure of how much observed data was accurately accounted for (i.e.correctly predicted) by the regression model (m). This is a very basicmeasure of the predictive accuracy of the regression model and isdescribed herein by way of example only. If desired, a moresophisticated approach, such as variance analysis, could also be used toaccurately measure the predictive power of a given regression model (m).

[0105] Variance analysis measures the variance in the criterion variable(e.g., Y′) as a function of each of the predictor variables (e.g., X₁,X₂, X₃). The measured variance in the criterion variable (Y') can bebroken into two parts: that predicted by one or more of the selectedpredictor variables and that variance not predicted by the selectedpredictor variables. The latter is often referred to as “errorvariance.” The total predicted variance is the amount of varianceaccounted for by the regression model. For instance, if the predictedvariance is 0.78—this means the regression model is accounting for 78%of the possible variance. Of course, it is important and desirable toaccount for as much variance as possible with a given regression model.The more variance one can account for, the more confidence one has aboutthe predictions made by the regression model.

[0106] Predicted variance can also be increased by adding more predictorvariables to the regression model. But, as the number of predictorvariables in the regression model increases beyond a certain point thereis a risk that the predicted variance may become artificially inflated,indicating that the model is purporting to account for variance that isnot actually accounted for in the population. This problem may becontrolled by selecting an appropriate number of predictor variables ina given model in accordance with the number of samples in thepopulation. Preferably, the number of predictor variables is no morethan about 5-10% of the total number of samples in a given populationand is most preferably less than about 1-3% of the total population.Thus, for a patent population size of 1,000, preferably the number ofpredictor variables is no more than about 50-100 and most preferably nomore than about 10 to 30 total, or between about 15-25. Alternatively,where it is desirable to use more predictor variables in a givenregression model, an adjusted predicted variance may be calculated usingwell-known techniques which take into account both the number ofpredictor variables and the sample size.

[0107] Decision block 230 compares the calculated statistical accuracySA(m) of the current regression model (m) to the statistical accuracySA(m−1) of the previous regression model (m−1). If the statisticalaccuracy SA(m) indicates improvement, then decision block 230 directsthe system to coefficient adjustment block 227. This block increments ordecrements one or more of the coefficients (a, b, c and d) by apredetermined amount (Δa, Δb, Δc and Δd). The adjustment amounts (+ or−) are periodically determined by the system 200 to accurately convergethe regression model toward maximum statistical accuracy SA. This may bedone in a variety of ways. One simple convergence technique is describedbelow.

[0108] If decision block 230 determines that SA(m)<SA(m−1), thisindicates that the current regression model (m) is a worse predictor ofthe desired patent quality than the previous regression model (m⁻¹).Therefore, a different adjustment is needed to be made to thecoefficients a, b, c, and/or d in order to cause the system toreconverge toward the optimal solution providing for maximum predictiveaccuracy. This is done by directing the system to blocks 232-268 to testthe impact of various changes to each predictor variable (a, b, c, d)and to change one or more of the coefficient adjustment amounts (Δa, Δb,Δc and Δd) as necessary to reconverge on the optimal solution.

[0109] Preferably, course adjustments are made first and then finer andfiner adjustments are continually made as the regression model convergeson an optimal solution having maximized statistical accuracy SA. Thus,decision blocks 232, 242, 252 and 262 first preferably determine whichof the adjustment amounts (Δa, Δb, Δc and Δd) is greatest in magnitude.For example, if it is determined that Δa is greater than each of theadjustment amounts Δb, Δc and Δd, then decision block 232 directs thesystem to block 234.

[0110] Block 234 tests a modified regression model (m⁻¹) where a=a-Δa/2.If the modified regression model results in improved statisticalaccuracy such that:

SA(TEST)>SA(m ⁻¹)

[0111] then decision block 236 directs the system to block 238. Block238 inverts and reduces the adjustment amount Δa=−(Δa/2) andreinitializes the counts CO and IN to zero. Block 240 reinitializes thepatent count to n=1. The system then resumes normal operation startingat block 206.

[0112] If the modified regression model does not result in improvedstatistical accuracy, decision block 236 directs the system to the nextdecision block 242 to determine whether an adjustment to one of theother coefficients might improve the accuracy of the regression model.The process of adjusting the coefficients and testing the accuracy of anew adjusted regression model repeats until decision block 262determines that the system has cycled through a predetermined number ofmodels, in this case m=1000. At this point the system stops at END block270, whereby the data may be extracted and studied or used to providequality ratings or rankings of patents outside (or inside) the studypopulations as described above. If there are any non-linearrelationships between the criterion variable and any predictorvariable(s), it is preferred to randomize the variable coefficients atleast periodically and reconverge toward an optimal solution in order tofully explore all possible optimal solutions.

[0113] Multiple regression modeling, as described above in connectionwith FIG. 2, is particularly well suited to carrying out the ratingmethods of the present invention. The methodology allows one not only todetermine a statistical relationship between a criterion variable (CV)of interest and a number of predictor variables (PVs), it also allowsone to determine the independent contributions of each predictorvariable in the model by allowing for partitioning of variance. In otherwords, one can determine how much variance in the criterion variable isaccounted for by a specific predictor variable. This can beaccomplished, for example, by removing the PV in question from the modeland then determining if the correlation predicted by the modelsignificantly declines when the predictor variable is removed from theequation and the other predictor variables remain.

[0114] Partitioning of variance is also useful in detecting possiblecollinearity or multi-collinearity between two of more predictorvariables. Collinearity occurs when all or most of the variance in onepredictor variable is accounted for by one other predictor variable.Multi-collinearity exists when several predictor variables combinedaccount for all or most of the variance of another predictor variable.While not directly detrimental to the utility of the invention,collinearity or multi-collinearity can create problems where it isdesired to accurately determine the slope or direction of an individualregression line for a particular predictor variable. Collinearity ormulti-collinearity can be reduced or eliminated by removing superfluouspredictor variables and/or by combining two or more predictor variablesinto a single normalized predictor variable.

EXAMPLE APPLICATIONS

[0115] Having thus described the preferred embodiments of the inventionin detail those skilled in the art will recognize that the basicconcepts and principles disclosed herein may be applied and implementedin a wide variety of useful ways to achieve desired results. A fewexamples are provided below by way of illustration in order todemonstrate the broader utility of the invention and how it may be usedcommercially.

Example 1

[0116] One possible application of the present invention is to identifyand study relevant characteristics from a sample of litigated patents todetermine and measure those patent metrics that are predictive of apossible future event, such as a patent being litigated. Patentlitigation is the ultimate attestation of patent value. A patentplaintiff is faced with enormous legal costs to bring and prosecute apatent infringement action. Thus, the decision to invest suchsubstantial sums to enforce a patent is potentially (although, notnecessarily) a strong indicator of the strength and value of theunderlying patent asset.

[0117] A study of statistical data representing about 1200 litigatedpatents reveals several interesting patterns which can help predictwhether a particular patent will be litigated. One pattern that isimmediately evident is that patents are typically litigated relativelyearly in their lives. FIG. 3 is a graph of the average age of a selectedsample of litigated patents. This graph indicates that most patents(>50%) that are litigated are litigated within five years from the dateof issuance. The decrease in the incidence of patent litigation with agesuggests that patents may have a diminishing value over time. This isgenerally consistent with what one might expect as newer technologyreplaces older technology. Thus, using the graph of FIG. 3 and knowingthe age of a particular patent(s) of interest (all other things beingassumed equal), one can estimate the probability of the patent(s) beinglitigated within one year, two years, three years, etc., in the future.

[0118] Another interesting pattern is that foreign originating patents(i.e., patents claiming priority to a foreign parent application) aremuch less likely to be litigated than domestic originating patents. Forexample, a study of the relevant data reveals that 0.67% of all patentsissued in 1990 were litigated, compared to 0.16% of foreign originatingpatents. Moreover the incidence of patent litigation variessignificantly with country of origin. Only 0.10% of all Japaneseoriginating patents issued in 1990 were litigated compared to 0.38% ofU.K. originating patents and compared to 0.15% of German originatingpatents. These differences may reflect disparities in the relative costsof litigation for various foreign patentees as well as language andcultural differences.

[0119] Each of the patent metrics identified above is anticipated tohave a statistically significant impact on the probability of a patentbeing litigated in the future. By undertaking a statistical study ofthese and other patent metrics and by constructing a suitable regressionmodel in accordance with the invention disclosed herein, one cancalculate an estimated statistical probability of a given patent beinglitigated during a predetermined period of time in the future based onthe identified patent characteristics. If desired, a numerical rating orranking may be assigned to each patent indicating the relativelikelihood of litigation.

Example 2

[0120] Another possible application of the present invention is toidentify and study relevant characteristics from a sample of litigatedpatents to determine and measure those patent metrics that arepredictive of a particular desired outcome in litigation (e.g., afinding of infringement and/or invalidity).

[0121] For example, it is a commonly-held notion among patentprofessionals that certain claim language or claim limitations can havenarrowing effects on the scope of patent claims. Claims that are verylong and recite many detailed limitations or that recite limitations inthe form of “means plus function” language and the like cansignificantly restrict the scope of patent claims. Therefore, it isanticipated that patent metrics reflecting such qualities (e.g., largenumber of words per claim, or large number of different words per claim,use of “means” language and the like) will have a statisticallysignificant negative correlation with favorable litigation results.

[0122] Table 1 below, summarizes the incidence of final judgments ofinfringement for 665 reported patent infringement cases brought in theU.S. federal district courts between 1987 and 1998. The results aredivided according to whether one or more of the asserted claim(s)contained a “means” limitation. TABLE 1 Asserted Claim % Infringed“Means” 47.1 “Non-Means” 51.2

[0123] As indicated in Table 1, above, asserted patent claims thatcontained at least one “means” limitation were found to be infringedabout 8.7% (4.1% in absolute percentage terms) less often than assertedpatent claims that did not contain a means limitation. This supports thenotion that “means” limitations have a narrowing effect on claimbreadth.

[0124] Similarly, FIG. 4 is a graph 320 of percentages of litigatedpatents found to be infringed by a federal district court between 1987and 1998, illustrating a statistical relationship between the incidenceof infringement and the average number of words or “word count” perindependent claim. The graph generally illustrates a declining incidenceof patent infringement with increasing word count. Again, this supportsthe generally-held notion that longer claims are narrower than shorterclaims. Of course, those skilled in the art will recognize that moresophisticated relationships could also be established and characterizedstatistically.

[0125] For example, a modified word count metric comprising onlynon-repeated words per claim could be used. Alternatively, each wordand/or word phrase in a patent claim could be assigned a point valueaccording to its frequency of use in a randomly selected population ofsimilar patents in the same general field. Statistically common words orword phrases such as simple articles, pronouns and the like wouldreceive relatively low point values. Uncommon words or word phraseswould receive relatively high point values. The total point score foreach claim would then be an indication of its relative breadth ornarrowness based on the total number and statistical prevalence of eachof the words contained in the claim. Optionally, different amounts ofpoints can be accorded to claim words or word phrases based on whetheror not such words or word phrases also appear in the patentspecification. Multiple claims and/or patents could also be combinedinto a single such analysis, if desired.

[0126] If multiple independent claims are being considered for eachpatent, it may be helpful to develop a “relatedness index” metric whichcharacterizes the relatedness of each claim to one or more other claimsof the patent (and/or one or more other patents). All other things beingequal, it is expected that a patent having two or more claims that arehighly related to one another (e.g., having substantially overlappingclaim coverage) would be narrower in overall scope than a patent havingtwo or more claims that are substantially dissimilar from one another(and, therefore, likely cover different subject matter). One convenientway to formulate a relatedness index is to compare the number of wordsthat are common to each claim versus the number of words that are uniqueto each claim. For example, a first claim of interest (claim 1)may-contain 95% of the same words in common with a second claim ofinterest (claim 2). Therefore, the two claims could be described ashaving a relatedness index (R_(1,2)) of 95% or 0.95. However, a thirdclaim of interest (claim 3) may contain only 45% of the same words incommon with the first claim (claim 1). Therefore, these two claims couldbe described as having a relatedness index (R_(1,3)) of 45% or 0.45.More sophisticated approaches could further weight or score each word inaccordance with frequency of use as described above, and/or couldprovide for matching of similar or synonymous words. A relatedness indexmetric could also be developed and used to compare the relatedness orapparent relatedness of one or more patent specifications. This could beuseful, for example, in identifying related or similar patents within aportfolio.

[0127]FIG. 5 is a graph 340 of litigated patents according to technicalfield, illustrating the incidence of patent infringement holdings byfield. Similarly, FIG. 6 is a graph 360 of litigated patents accordingto technical field, illustrating the incidence of patent invalidityholdings by field. In each case, the numbers above each bar indicate thesample size of each patent population reported. Each of these graphsillustrates a statistical relationship between the general technicalfield of an invention and the incidence of validity or infringementholdings in litigation.

[0128]FIG. 7 is a graph 380 of percentages of litigated patents found tobe invalid by a federal district court according to the average age ofU.S. patent references cited therein. In particular, the graph 380illustrates a declining incidence of patent invalidity with citationage. Curve 390 is a representative trend line having the generalequation:

Y=m×+B

[0129] where:

[0130] Y=Y-coordinate value (% infringement)

[0131] X=X-coordinate value (avg. age cited refs. in years)

[0132] m=slope of line (% infringement/#years)

[0133] b=Y-axis intercept

[0134] The slope (m) and Y-axis intercept (b) of curve 390 weredetermined by trial and error to produce an ordinary least squares fitto the data reported by graph 380. Thus, the curve 390 (and theresulting formula thereof) is generally representative of thestatistical relationship between average citation age and incidence ofpatent validity in litigation.

[0135] In each of the cases described above, the identified patentmetrics are anticipated to have a statistically significant impact onthe probability of a patent being litigated successfully orunsuccessfully. By undertaking a statistical study of these and otheridentified patent metrics and by constructing a suitable regressionmodel in accordance with the invention disclosed herein, one canaccurately calculate an estimated statistical probability of a givenpatent being successfully litigated (found valid and/or infringed),taking into consideration all of the identified patent characteristicsand statistical relationships simultaneously. If desired, a numericalrating or ranking may be automatically calculated and assigned to eachpatent indicating the relative likelihood of a particular event orquality. Such rating may be provided for the patent as a whole or,alternatively (or in addition), individual ratings may be provided forone or more individual claims of the patent, as desired.

Example 3

[0136] In the United States and most foreign countries, patentees arerequired to pay periodic maintenance fees during the term of a patent tomaintain the patent in force. In most countries, these consist of fixedannual fees of $200-300 per year paid to the government patent office tomaintain a patent in force. In the United States, maintenance fees arepaid every four years and escalate progressively from $525/$1,050 tomaintain a patent in force beyond the fourth year, to $1,050/$2,100 tomaintain a patent in force beyond the eighth year, to $1,580/$3,160 tomaintain a patent in force beyond the twelfth year. Patentees thatqualify as a “small entity” pay the smaller amounts; all others pay thelarger amounts.

[0137] The relatively substantial and escalating nature of theseperiodic maintenance fee payments has the effect of discouraging themaintenance for the full-term of all but the most successful or valuablepatents. Thus, such patent maintenance fee data provides a unique,introspective look at how patentees themselves value their own patents.A reasonable and economically motivated patentee would not pay tomaintain his or her patent if the cost of the maintenance fee exceededthe reasonable expected future benefit likely to be gained bymaintaining the patent in force for an additional four year period.Thus, PTO records reflecting the payment or non-payment of periodicmaintenance fees by patentees provides a wealth of data from which awide variety of useful information may be derived. Such information isuseful, for example, for purposes of conducting patent valuations,patent rankings, patent ratings, and/or for other purposes as generallytaught herein.

[0138] Thus, another possible application of the present invention is toidentify and study relevant characteristics of a sample population of20,000-80,000 patents that have been maintained beyond the first, secondor third maintenance periods as against a sample population of20,000-80,000 patents that have not been maintained or are abandonedprior to the expiration of their statutory term. In this manner, one maydetermine and measure with a high-level of statistical accuracy (i.e.,greater than 95% confidence) those patent metrics that are predictive ofpatents being abandoned prior to expiration of their full term.Moreover, one may determine with a similar degree of statisticalaccuracy the particular relationship or contribution provided by one ormore individual patent metrics of interest. This may be accomplished,for example, using variance partitioning and/or other similarstatistical analysis techniques.

[0139] In this case, a study of the statistical data reveals severalinteresting patterns that may help predict whether a particular patentwill be abandoned or maintained beyond its first, second or thirdmaintenance period. FIG. 8 is a graph of patent maintenance rates for arandom sample population of patents issued in 1986. This graph 400indicates that approximately 83.5% of such patents were maintainedbeyond the fourth year, approximately 61.9% of the patents weremaintained beyond the eighth year and approximately 42.5% of the patentswere maintained beyond the twelfth year. In other words, all but about42.5% of the sample population were abandoned or allowed to expirebefore the full statutory patent term. This corresponds to an overallaverage patent mortality (abandonment) rate of approximately 58.5%. Fromthis and/or other similar data one can formulate certain generalexpectations or probabilities as to whether a patent will likely bemaintained or abandoned in the future.

[0140] More specific expectations and probabilities can be formulated byidentifying and/or measuring those specific patent metrics associatedwith patent populations having either high or low mortality rates. Forexample, the data reveals that Japanese originating patents generallyhave lower mortality rates than domestic originating patents (44.7% vs.58.5%). The data also reveals that patents classified by the PTO indifferent classes and/or subclasses can have significantly differentmortality rates. For example, Table 2 below illustrates various observedmortality rates for patents categorized in several selected PTO classes:TABLE 2 CLASS DESCRIPTION MORTALITY 482 Exercise Equipment 79% 473 GolfClubs/Equipment 74% 434 Golf Training Devices 71% 446 Toys and AmusementDevices 70% 206/250 Packaging 57% 365/364 Computers 45% 935 GeneticEngineering 44%

[0141] As Table 2 illustrates, patent mortality rates can varydramatically depending upon the general subject matter of the patentedinvention as determined by the PTO classification system. Thus, one canreasonably conclude that, all other things being equal, certain classesof inventions are probably more valuable (more likely to be maintained)or less valuable (less likely to be maintained) than certain otherclasses of inventions. From this and/or other similar data one canformulate specific and/or more accurate expectations or probabilities asto whether a particular patent having certain identified characteristicswill likely be maintained or abandoned in the future.

[0142]FIG. 9 illustrates a similar observed correlation between thenumber of claims contained in a patent and the patent mortality rate. Inparticular, for patents having five or fewer claims the averagemortality rate is observed to be about 66.3%. However, for patentshaving greater than 25 claims the mortality rate is observed to drop to49.3%. Again, this indicates that, all other things being equal, patentshaving more claims are probably more valuable (more likely to bemaintained) than patents having less claims.

[0143]FIG. 10 illustrates another similar observed correlation betweenthe number of figures or drawings contained in a patent and the patentmortality rate. In particular, for patents having five or fewer figuresthe average mortality rate is observed to be about 62.7%. However, forpatents having greater than 25 figures the mortality rate is observed todrop to 46.6%. Again, this indicates that, all other things being equal,patents having more figures (and presumably more disclosure) areprobably more valuable (more likely to be maintained) than patentshaving less figures.

[0144] At least one study has reported that the number of citationssubsequently received by a patent (“forward” citations) may also have apositive correlation with economic value. See, e.g., Harhoff et al.,“Citation Frequency and the Value of Patented Innovation, ZEW DiscussionPaper No. 97-27(1997). Assuming this is true, one would expect to see arelatively low mortality rate for patents that receive an above-averagenumber of forward citations and a relatively high mortality rate forpatents that receive a below-average number of forward citations. Thiscan be easily verified and statistically measured using the methodstaught herein.

[0145] Each of the patent metrics identified above is anticipated tohave a statistically significant impact on the probability of a patentbeing maintained or abandoned, litigated successfully or unsuccessfully,etc. By undertaking a statistical study of these and other patentmetrics and by constructing a suitable regression model or algorithm inaccordance with the invention disclosed herein, one can calculate with astatistically determined accuracy an estimated probability of aparticular patent quality or a particular event occurring affecting agiven patent. If desired, a numerical rating or ranking may be assignedto each patent indicating its relative value or score. Multiple ratingsor rankings may also be provided representing different qualities ofinterest or probabilities of particular future events occurring.

[0146] Patent Ratings, Valuations & Reports

[0147] Patent ratings or rankings as taught herein may be compiled andreported in a variety of suitable formats, including numericalratings/rankings, alphanumeric ratings/rankings, percentile rankings,relative probabilities, absolute probabilities, and the like. Multipleratings or rankings may also be provided corresponding to differentpatent qualities of interest or specific patent claims. FIG. 11illustrates one possible form of a patent rating and valuation report700 that may be generated in accordance with a preferred embodiment ofthe invention.

[0148] As illustrated in FIG. 11, the report 700 contains some basicdata 710 identifying the patent being reported, including the patentnumber, title of the invention, inventor(s), filing date, issue date andassignee (if any). Several individual patent ratings 720 are alsoprovided, including overall patent breadth (“B”), defensibility (“D”),and commercial relevance (“R”). Breadth and Defensibility ratings arepreferably generated by a computer algorithm that is selected andadjusted to be predictive of known litigation outcomes (e.g.,infringement/non-infringement and validity/invalidity) of a selectedpopulation of litigated patents based on various comparative patentmetrics. Relevance ratings are preferably generated using a computeralgorithm selected and adjusted to be predictive of patent maintenancerates and/or mortality rates based on various comparative patent metricsincluding, preferably, at least one comparative metric based on anormalized forward patent citation rate (normalized according to patentage). If desired, each of the B/D/R ratings can be statisticallyadjusted relative to the remaining ratings using known statisticaltechniques so as to minimize any undesired collinearity or overlap inthe reported ratings.

[0149] In the particular example illustrated, ratings 720 are providedon a scale from 1 to 10. However, a variety of other suitable ratingscales may also be used with efficacy, such as numerical rankings,percentile rankings, alphanumeric ratings, absolute or relativeprobabilities and the like. If desired, individual ratings or rankings720 may also be combined using a suitable weighting algorithm or thelike to arrive at an overall score or rating 730 for a given patent,patent portfolio or other intellectual property asset. The particularweighting algorithm used would preferably be developed empirically orotherwise so as to provide useful and accurate overall patent ratinginformation for a given application such as investment, licensing,litigation analysis, etc.

[0150] For investment purposes, for example, overall ratings may beprovided in the form of convenient bond-style ratings as summarized inTable 3 below: TABLE 3 Quality Rating Highest quality AAA High qualityAA Medium-high quality A Upper medium quality BBB Medium quality BBLower medium quality B Medium-low quality CCC Low quality CC Lowestquality C

[0151] If desired, such overall ratings can be separately collected andtabulated for use as a handy reference source. For example, overallpatent ratings can be published and updated periodically for all patentscurrently in force and/or for all newly issued patents published by thePTO, providing simple and useful information to those who desire to useit. Such information could also advantageously be stored on a searchabledatabase accessible through an Internet-based web server or the like.

[0152] To accomplish this purpose, the invention may be modified andadapted to provide high-speed, automated scoring or rating of asequential series of newly issued patents periodically published by thePTO. According to the preferred method, a substantial full-text copy ofeach patent in the sequential series is obtained in a computer text fileformat or similar computer-accessible format. A computer program iscaused to automatically access and read each computer text file and toextract therefrom certain selected patent metrics representative of ordescribing particular observed characteristics or metrics of each patentin the sequential series. The extracted patent metrics are input into apreviously determined computer regression model or predictive algorithmthat is selected and adjusted to calculate a corresponding rating outputor mathematical score that is generally predictive of a particularpatent quality of interest and/or the probability of a particular futureevent occurring. Preferably, for each patent in the sequential series arating output or mathematical score is directly calculated from theextracted metrics using a series of predefined equations, formulasand/or rules comprising the algorithm. The results are then preferablystored in a computer accessible memory device in association with otherselected information identifying each rated patent such that thecorresponding rating may be readily referenced or retrieved for eachpatent in the sequential series.

[0153] Because the rating method in accordance with the modifiedembodiment of the invention described above directly calculates (foreach patent or group of patents) the mathematical score or rating fromthe patent metrics themselves, there is no need to access related storeddata, such as comparative representative patent data, from an associateddatabase. Thus, the method can be carried out very rapidly for eachpatent in the sequential series. For example, using a high-speedcomputer executing a predetermined predictive algorithm the automatedrating method described above can preferably be carried out in less thanabout 1-3 minutes per patent, more preferably in less than about 30-45seconds per patent, and most preferably in less than about 5-10 secondsper patent. Moreover, because the predictive algorithm operates withoutrequiring access to any comparative representative data, it may beeasily stored, transferred, transported or otherwise communicated toothers without the need to also store, transfer, transport orcommunicate the underlying comparative data used to develop thealgorithm.

[0154] While it is preferred to provide independent B/D/R ratings and/oran overall score for each rated patent asset, those skilled in the artwill recognize that numerous other ranking or rating systems may be usedwith efficacy in accordance with the teachings herein. For example,individual patent/claim scores may be ranked relative to a givenpopulation such that ratings may be provided on a percentile basis.Alternatively, numerical and/or alphanumerical scores may be assigned ona scale from 1-5, 1-9, 1-10, or A-E, for example. Optionally, and asillustrated in FIG. 11, each claim of the reported patent may beanalyzed and rated separately if desired. In that case, each claim (1-9in the example illustrated in FIG. 11) is preferably indicated as beingeither independent (“I”) or dependent (“D”), as the case may be.Alternatively, only the independent claims of a reported patent may berated if desired.

[0155] Individual ratings 740, 750 and 755 in report 700 preferablyprovide numerical ratings (1-10) of the likely breadth (“B”),defensibility (“D”), and relevance (“R”) of each claim of the reportedpatent (and/or the patent as a whole). Such “BDR” ratings mayalternatively be expressed in a variety of other suitable formats, suchas letters, symbols, integer numerals, decimal numerals, percentageprobabilities, percentile rankings, and the like. For example, a letterscoring system (e.g., A-E) could be assigned for each of the individualB/D/R components. In that case, a BDR rating of “B/A/A” would representa “B” rating for breadth, and “A” ratings for both defensibility andrelevance. An overall rating could then be derived from the individualBDR component ratings using a suitable conversion index rating system asgenerally illustrated below in Table 4: TABLE 4 BDR Rating OverallRating A/A/A AAA A/A/x AA A/x/A AA x/A/A AA A/x/x A x/A/x A x/x/A AB/B/B BBB B/B/x BB B/x/B BB x/B/B BB B/x/x B x/B/x B x/x/B B C/C/C CCCC/C/x CC C/x/C CC x/C/C CC C/x/x C x/C/x C x/x/C C x/x/x D

[0156] In the above Table 4, “x” represents an individual componentrating (either B, D or R) that is lower than the highest of theremaining rating component(s) such that only the highest componentrating(s) are reflected in the overall rating. Thus, a BDR rating ofA/A/B or A/B/A would each produce an overall rating of “AA.” Likewise, aBDR rating of C/B/C or B/D/E would each produce an overall rating of“B.” Optionally, various additional rules and/or weighting formulas maybe used to adjust the overall rating assigned in accordance with thissystem. For example, if one or more of the low component ratings “x” istwo or more rating levels below the highest component rating(s) then theoverall rating can be decreased by one increment. Thus, a BDR rating ofC/B/C would produce an overall rating of “B” whilst a BDR rating ofB/D/E would produce an overall rating of “CCC” or “CC”. Preferably, ifno individual component rating is at least a “C” (or other predeterminedrating level) or higher, then the overall rating is assigned somearbitrary baseline rating, such as “D” or “C” or “S” and/or the like.

[0157] Preferably, estimated maintenance rates 760 are also provided andare indicated as percentage probabilities for each maintenance period.Alternatively, maintenance data may be provided in a number of othersuitable formats, as desired, such as percentile rakings, absolute orrelative probabilities and the like. Also, various confidence levels maybe calculated and displayed for each of the reported probabilities 760,if desired.

[0158] Optionally, the report 700 may further include an estimatedvaluation range 770 or expected value of the reported patent. Suchpatent valuation 770 may be based on a variety of suitable techniquesthat preferably take into account the rating information providedherein. For example, a modified cost-basis approach could be usedwhereby the cost-basis is multiplied by a suitable discount orenhancement factor corresponding to the rating(s) that the patentreceives in accordance with the methods disclosed herein. In thismanner, patents that receive higher-than-average ratings would be valuedat more than their cost basis. Conversely, patents that receivelower-than-average ratings would be valued at less than their costbasis.

[0159] Similarly, a modified income valuation approach could be usedwhereby a hypothetical future projected income stream or averageindustry royalty rate is multiplied by a suitable discount orenhancement factor corresponding to the rating that the patent receivesin accordance with the methods disclosed herein. In this manner, patentsthat receive higher ratings would be valued at higher than industryaverages. Conversely, patents that receive lower ratings would be valuedat lower than industry averages.

[0160] Another preferred approach would be to allocate patent valuebased on a percentile ranking of patents as determined herein. For thisapproach an approximated distribution of relative patent values isdetermined from existing patent renewal data, patent litigation dataand/or the like. From this data, a value distribution curve can beconstructed such as illustrated in FIG. 12. The shape of the curvegenerally represents an estimated distribution (e.g., on a percentilebasis) of approximated patent values spread over a range from the veryhighest-value patents to the very lowest-value patents. See also, Hall,“Innovation and Market Value,” Working Paper 6984 NBER (1999)(suggesting an extremely skewed value distribution whereby a few patentsare extremely valuable, while many others are worth little or almostnothing). The area under the curve 800 preferably corresponds to thetotal estimated value of all patents in a given patent population (e.g.,all U.S. patents currently in force). This can be readily estimated orapproximated by applying suitable macro-economic analysis. For example,it may be approximated as a percentage of total GNP, or as a percentageof total market capitalization of publicly traded companies, or as amultiple of annual budgeted PTO fees and costs, and/or the like.

[0161] Patents having the highest percentile rankings in accordance withthe rating methods disclosed herein would then be correlated to the highend of the value distribution curve 800. Conversely, patents having thelowest percentile rankings in accordance with the rating methodsdisclosed herein would then be correlated to the low end of the valuedistribution curve 800. Advantageously, such allocative valuationapproach brings an added level of discipline to the overall valuationprocess in that the sum of individual patent valuations for a givenpatent population cannot exceed the total aggregate estimated value ofall such patents. In this manner, fair and informative valuations can beprovided based on the relative quality of the patent asset in questionwithout need for comparative market data of other patents or patentportfolios, and without need for a demonstrated (or hypothetical) incomestreams for the patent in question. Estimated valuations are basedsimply on the allocation of a corresponding portion of the overallpatent value “pie” as represented by each patents' relative ranking orposition along value distribution curve 800.

[0162] Alternatively, any one or more of the above valuation techniques(or other techniques) can be combined or averaged to produce appropriatevaluation ranges and/or various blended valuation estimates, as desired.Various confidence levels may also be calculated and reported for eachof the reported value ranges 770. Alternatively, several different valueranges can be calculated according to different desired confidencelevels.

[0163] Internet Applications

[0164] The present invention is ideally suited for Internet-basedapplications. In one preferred embodiment, the invention would be madeavailable to Internet users on the World Wide Web (“the web”), or asimilar public network, and would be accessible through a web page.Various services, embodying different aspects of the present invention,could be made available to users on a subscription or a pay-per-usebasis.

[0165] In an Internet-based application, users would preferably haveaccess to automated patent ratings, consolidated patent ratings (i.e.grouped by technology, business sector, industry, etc.), and a host ofancillary information regarding particular patents or groups of patents.Ancillary information may include, for example, full-text searchablepatent files, patent images, bibliographic data, ownership records,maintenance records, and the like. A user would preferably be able toenter or “click” on the number of a patent he or she was interested inand obtain, in very short order (e.g., in less than about 1-5 minutes),a comprehensive rating report as described above. Preferably, the userwould be able to control most, if not all, of the variables in therating calculation. Thus, for instance, he or she could request that thepatent be rated only against other patents in the same art group, or ina specific industry, or in a particular field of use. He or she couldrequest a report on how the patent compares to all patents that havebeen litigated in the past 5 years, or that have been held invalid byU.S. courts. In this manner, reports could be narrowly tailored to thespecific interests and concerns of the user. This is beneficial—thoughnot critical—because different types of users, e.g., lawyers,businessmen, manufacturers, investors, etc., will have slightlydifferent appraisal needs.

[0166] In another preferred embodiment, it is not necessary that a useractually know the patent number or title of the patent he or she wishesto have rated. Instead, this preferred embodiment would include a seriesof correlation tables which allow the user to retrieve patent numbersbased on ownership, field of use, or even specific commercial products.Thus, it would be possible for a user to request reports on all patentsthat have been issued or assigned to a particular company in the past 5years.

[0167] Ideally, it would also be possible for a user to request reportson all patents associated with a specific commercial product. Suchproduct patent information could advantageously be collected and storedon a centralized, searchable computer network database or the like inorder to allow users to search and obtain patent information onparticular commercial products. Relevant patent marking data could begathered either through private voluntary reporting by manufacturers ofsuch products and/or it may be gathered through other available means,such as automated web crawlers, third-party reporting or inputting andthe like. Patent marking data (e.g., the presence or absence of a patentnotice on a corresponding commercial product) and/or other relevant data(e.g., sales volume, sales growth, profits, etc.) could provideadditional objective metric(s) by which to rate relevant patents inaccordance with the invention. Presumably, patents that are beingactively commercialized are more valuable than “paper patents” for whichthere is no corresponding commercial product. Optionally, the patentmarking database can also include the necessary URL address informationand/or the like which will allow users to hot-link directly to athird-party web page for each corresponding product and/or associatedproduct manufacturer.

[0168] In another embodiment of the invention, users would be allowed torequest automatic updates and patent ratings according to certainuser-defined parameters. Thus, a user who is particularly interested inthe XYZ company could request an automatic updated report—sent to himsubstantially contemporaneously (preferably within a few days, morepreferably within about 2-3 hours, and most preferably within less thanabout 5-10 minutes) via e-mail and/or facsimile—whenever the XYZ companyobtains a newly issued patent. A similar updated report could begenerated and sent any time a new patent issued or a new application ispublished in a particular technology field or class of interest. Theupdates would preferably contain a synopsis of each new patent orpublished application, as well as a patent rating performed according tothat user's preferred criteria. Updated reports for each rated patentcould also be generated periodically whenever one or more identifiedpatent metrics changed (e.g., forward citation rate, change ofownership, litigation, etc.). Such automated updating of ratinginformation would be particularly important to investment and financialanalysts, who depend on rapid and reliable information to makeminute-by-minute decisions. Updated report(s) could also be generatedand published each week for all newly issued patents granted by the PTOfor that current week. Thus, in accordance with one preferred embodimentof the invention, informative patent rating and/or ranking informationmay be provided within days or hours of a new patent being issued andpublished by the PTO.

[0169] Another service that may be provided in a preferredInternet-based application of this invention is a user-updatedinformation database. According to this embodiment, certain users and/orall users would be allowed to post information they believe is pertinentto a particular patent or group of patents. Such information mightinclude prior art that was not cited in the patent, possible licenseterms, potential problems with the written description or claims of thepatent, information about the inventors, information relating to salesof patented products prior to the filing date, legal opinions, relatedlitigation, and any other information that might be relevant to thepatent. The information would preferably be stored and displayed inassociation with each particular patent to which it is relevant. Thus,from the user's perspective each patent would, in effect, have its ownbulletin board or note pad associated with it, upon which users may postrelevant information. Other information could also be displayed, such aslicense terms available, commercial product information, other patentsof interest, electronic file wrappers, hot-links to other sites, and thelike.

[0170] Optionally, submitters could also provide their own rating orranking of the patent in question, such that patents could beessentially self-rated by users. In the preferred embodiment, onlyqualified users (or selected patent analysts) would be allowed to postsuch ratings. The qualification process could be as simple as fillingout a questionnaire or as thorough as an independent verification ofcredentials. It is also possible to employ the methodology currentlyused by such web sites as “epinions.com” to track the popularity andveracity of individual user-submitted information and determine whichusers are most trusted. Those users that are most trusted would bebrought to the top of the patent information database and their authorscompensated according to the number of times users accessed theinformation, while less-popular submitters' information would sink inrank. Users and/or analysts could also be compensated financially (orotherwise) based on the accuracy of their ratings relative to thecollective rating prediction and/or relative to the occurrence of apredicted future event. This would motivate more careful analysis andmore accurate ratings. See, U.S. Pat. No. 5,608,620, incorporated hereinby reference, for a description of a collective prediction andforecasting method using multiple individual forecasters, which may bereadily adapted and applied to the present invention as disclosedherein.

[0171] The present invention is also well suited for incorporation intoa newsletter service, such as the numerous financial newsletterscurrently available to Wall Street investors. In this embodiment of theinvention, the rating system described herein would preferably beapplied to a pre-defined subset of issued patents—for instance, allpatents newly issued to “Fortune 500” companies or designated “Pre-IPO”companies. Overall patent ratings would be denoted with a standardizedsystem, such as a 1-10 scale, four stars, bond-style ratings, “BDR”ratings and/or the like. Preferably, requested reports would beautomatically generated and e-mailed to each subscriber on a periodicbasis and/or on an event-triggered basis, as desired. In this way,subscribers would be provided with a standardized method of comparingpatent portfolios of various companies from week to week.

[0172] While the statistical rating method and system of the presentinvention is disclosed and discussed specifically in the context ofrating utility patents, those skilled in the art will readily appreciatethat the techniques and concepts disclosed herein may have equalapplicability to rating other types of intellectual property assets,such as trademarks, copyrights, trade secrets, domain names, web sitesand the like. Moreover, although this invention has been disclosed inthe context of certain preferred embodiments and examples, it will beunder-stood by those skilled in the art that the present inventionextends beyond the specifically disclosed embodiments to otheralternative embodiments and/or uses of the invention and obviousmodifications and equivalents thereof. Thus, it is intended that thescope of the present invention herein disclosed should not be limited bythe particular disclosed embodiments described above, but should bedetermined only by a fair reading of the claims that follow.

What is claimed is:
 1. A method for estimating the probability of a future event occurring relative to a particular identified intellectual property asset or group of intellectual property assets of interest, comprising: storing a first series of data comprising selected metrics identifying and/or quantifying certain selected characteristics of a first population of intellectual property assets for which the event has occurred; storing a second series of data comprising selected metrics identifying and/or quantifying said selected characteristics of a second population of intellectual property assets for which the event has not occurred or for which it is undetermined whether the event has occurred; constructing a predictive computer model or algorithm based on said stored first and second series of data, said algorithm being operable to retrieve said first and second series of stored data and to perform certain mathematical or statistical calculations thereon to generate an output score or estimated probability that is generally predictive of the event having either occurred or not occurred relative to each intellectual property asset in said first or second populations of intellectual property assets; and providing as input to said algorithm a third series of data comprising selected metrics identifying and/or quantifying certain selected characteristics of said particular identified intellectual property asset or group of intellectual property assets of interest and operating said computer model to calculate a relative ranking or estimated probability of the event occurring in the future relative to said identified intellectual property asset or group of intellectual property assets of interest.
 2. The method of claim 1 wherein said first and/or second populations of intellectual property assets comprise patents that have been the subject of prior litigation through final judgment and wherein said event comprises one or more of the following: a final judgment of infringement, a final judgment of non-infringement, a final judgment of invalidity, a final judgment of non-invalidity, a final judgment in favor of the patentee, a final judgment in favor of the accused.
 3. The method of claim 1 wherein said first and/or second populations of intellectual property assets comprise selected samples of issued U.S. patents and wherein said event comprises one or more of the following: payment of the first scheduled maintenance fee, payment of the second scheduled maintenance fee, payment of the third scheduled maintenance fee.
 4. The method of claim 1 wherein said first and second populations of intellectual property assets have roughly the same population size.
 5. The method of claim 4 wherein said first and second populations of intellectual property assets have a population size of greater than about
 1000. 6. The method of claim 5 wherein said first and second populations of intellectual property assets have a population size of between about 20,000 and 80,000.
 7. The method of claim 1 wherein said selected metrics comprise one or more characteristics of each said intellectual property asset in said first and second populations of intellectual property assets that are determined or assumed to have either a positive or negative correlation with the occurrence or non-occurrence of said event.
 8. The method of claim 1 wherein said algorithm comprises a multiple regression model that correlates multiple individual predictor variables comprising said selected metrics to a single desired criterion variable comprising the approximated probability of said event either occurring or not occurring.
 9. The method of claim 8 wherein said multiple regression model has the form: CV _(m) =ƒ{PV ₁ , PV ₂ . . . PV _(n)}where: CVm=criterion variable or quality/event desired to be predicted  PV_(n)=predictor variables or selected patent metrics.
 10. The method of claim 9 wherein said regression includes between about 15 and 25 predictor variables.
 11. The method of claim 10 comprising the further step of determining the statistical accuracy of the regression model in accordance with the general formula: SA(m)=CO/(CO+IN) where: SA(m)=statistical accuracy of regression model (m)  CO=number of correct predictions for model (m)  IN=number of incorrect predictions for model (m).
 12. The method of claim 11 comprising the further steps of: incrementally modifying the regression model (m) to produce a modified regression model (m+1); determining the statistical accuracy of the modified regression model (m+1); comparing the statistical accuracy of the modified regression model (m+1) to the previously determined statistical accuracy of regression model (m); and either repeating said incremental modification of the regression model (m+1) to produce a further modified regression model (m+2) if the determined statistical accuracy of the modified regression model (m+1) is greater than the determined statistical accuracy of the regression model (m), or reversing said incremental modification of regression model (m+1) to produce the original regression model (m) if the determined statistical accuracy of the modified regression model (m+1) is less than the determined statistical accuracy of the regression model (m).
 13. The method of claim 1 comprising the further step of generating an event probability report for said identified selected intellectual property asset or group of intellectual property assets of interest, said report including basic information identifying said selected intellectual property asset or group of intellectual property assets of interest and said estimated probability of said event occurring or not occurring within a predetermined period of time in the future.
 14. An event probability report generated according to the method of claim
 13. 15. The method of claim 1 comprising the further steps of: providing data representative of an intellectual property asset value distribution curve, the shape of the curve generally representing an approximated distribution of patent value according to an estimated probability of said event either occurring or not occurring; and using said representative data to estimate a value or value range for said selected intellectual property asset of interest according to the estimated probability of said event either occurring or not occurring relative to said selected intellectual property asset of interest.
 16. An automated method for enabling a user to access and operate a predetermined predictive computer model or algorithm to score or rate a selected patent or group of patents in accordance with user-defined patent metrics, comprising: selecting a patent or group of patents to be rated; obtaining for each selected patent or group of patents a substantial full-text computer accessible file of the specification and/or claims thereof; extracting from each said file certain patent metrics, either predetermined or as selected or directed by said user; inputting said patent metrics into said predictive computer model, said computer model being operable to input said patent metrics and based thereon to calculate a corresponding output rating or probability that is generally predictive of a particular quality being present and/or an event occurring relative to the selected patent or group of patents; and displaying the resulting output rating or probability such that it may be viewed by said user.
 17. The method of claim 16 comprising the further step of ranking said output rating or probability against other output ratings or probabilities calculated for other patents within a predetermined or user-defined patent population.
 18. The method of claim 17 comprising the further step of storing relevant user-provided information pertaining to said selected patent or group of patents in a computer-accessible database in association with other information identifying said selected patent or group of patents.
 19. The method of claim 18 wherein said user-provided information comprises patent marking information and/or information pertaining to patented products.
 20. The method of claim 18 wherein said user-provided information comprises one or more of the following: relevant prior art that was not cited in the selected patent or group of patents, possible license terms, information about named inventors, information relating to sales of patented products, legal opinions, related litigation, other related or unrelated patents of interest, electronic file wrappers, hot-links to other web sites.
 21. The method of claim 18 wherein said user-provided information comprises user-provided ratings or rankings of said selected patent or group of patents.
 22. The method of claim 18 comprising the further step of displaying said stored user-provided information pertaining to said selected patent or group of patents in association with said selected patent or group of patents such that from the user's perspective each selected patent would, in effect, have its own bulletin board or note pad associated with it, upon which users may post and/or obtain relevant information.
 23. The method of claim 18 wherein said extracted patent metrics comprise one or more characteristics of said selected patent or group of patents that are determined or assumed to have either a positive or negative correlation with the presence or absence of said particular quality and/or said event occurring relative to the selected patent or group of patents.
 24. The method of claim 16 wherein said extracted patent metrics include one or more of the following: number of claims per patent, number of words per claim, different words per claim, length of patent specification, number of drawing pages or figures, number of cited prior art references, age of cited references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent, payment of maintenance fees, name of prosecuting attorney or law firm, examination art group, or length of pendency in the PTO.
 25. The method of claim 16 wherein said extracted patent metrics include one or more of the following: patent marking data, claim relatedness, patent relatedness, or claim type.
 26. The method of claim 16 wherein at least one of said extracted patent metrics comprises a modified claim word-count metric whereby each word and/or word phrase in a patent claim of interest is assigned a certain point value generally proportional to its determined frequency of use in a relevant patent population and wherein the word-count metric is set equal to the sum of each of the individual word point values for essentially all of the words or word phrases contained within said claim.
 27. The method of claim 16 wherein at least one of said extracted patent metrics comprises a relatedness metric generally indicative of the commonality of word or word phrase usage between one or more patent claims and/or patent specifications.
 28. The method of claim 16 wherein said predictive computer model comprises a multiple regression model that correlates multiple individual predictor variables comprising said extracted patent metrics to one or more desired criterion variables comprising the corresponding output rating or probability.
 29. The method of claim 28 wherein said multiple regression model has the form: CV _(m) =ƒ{PV ₁ , PV ₂ . . . PV _(n)}where: CV_(m)=criterion variable or quality/event desired to be predicted  PV_(n)=predictor variables or selected patent metrics.
 30. The method of claim 28 wherein said regression model includes between about 15 and 25 predictor variables.
 31. The method of claim 28 comprising the further step of determining the statistical accuracy of the regression model in accordance with the general formula: SA(m)=CO/(CO+IN) where: SA(m)=statistical accuracy of regression model (m) CO=number of correct predictions for model (m) IN=number of incorrect predictions for model (m).
 32. The method of claim 31 comprising the further steps of: incrementally modifying the regression model (m) to produce a modified regression model (m+1); determining the statistical accuracy of the modified regression model (m+1); comparing the statistical accuracy of the modified regression model (m+1) to the previously determined statistical accuracy of regression model (m); and either repeating said incremental modification of the regression model (m+1) to produce a further modified regression model (m+2) if the determined statistical accuracy of the modified regression model (m+1) is greater than the determined statistical accuracy of the regression model (m), or reversing said incremental modification of regression model (m+1) to produce the original regression model (m) if the determined statistical accuracy of the modified regression model (m+1) is less than the determined statistical accuracy of the regression model (m).
 33. The method of claim 16 comprising the further step of generating a patent rating report for said selected patent or group of patents, said report including basic information identifying said selected patent or group of patents and said corresponding output rating or probability determined therefor.
 34. The method of claim 33 wherein said patent rating report is generated in response to an electronic request transmitted over a computer network and wherein said report is generated and displayed automatically without further human intervention.
 35. The method of claim 33 comprising the further step of, after generating said report, automatically without further human intervention transmitting said report electronically over a computer network to one or more intended recipients.
 36. The method of claim 33 wherein said patent rating report contains at least one reported rating or ranking that is generally representative of the breadth (“B”) or likely infringement of the selected patent or group of patents, at least one reported rating or ranking that is generally representative of the defensibility (“D”) or likely validity of the selected patent or group of patents, and at least one reported rating or ranking that is generally representative of the commercial relevance (“R”) or technical merit of the selected patent or group of patents.
 37. A method for estimating or rating the probability of a future event occurring relative to a patent or group of patents, said method comprising: gathering data comprising two or more selected metrics generally identifying and/or quantifying certain selected characteristics of said patent or group of patents; providing said data as input to a mathematical estimating model; using the mathematical estimating model to calculate a numerical output based on the data input, said numerical output being generally predictive of said future event occurring relative to said patent or group of patents; and wherein said mathematical estimating model is derived from statistical data correlating said two or more selected patent characteristics to the occurrence or non-occurrence of said event
 38. The method of claim 37 wherein said mathematical estimating model is derived by storing a first group of data comprising selected metrics identifying and/or quantifying said selected characteristics of a first population of patents for which the event has occurred, storing a second group of data comprising selected metrics identifying and/or quantifying said selected characteristics of a second population of patents for which the event has not occurred or for which it is undetermined whether the event has occurred, and using a statistical regression to create a predictive algorithm based on said stored first and second groups of data, said algorithm being operable to perform certain mathematical or statistical calculations using said first and second groups of stored data and to generate in each case an output score or estimated probability that is generally predictive of the event having either occurred or not occurred relative to each patent or group of patents.
 39. The method of claim 37 wherein said future event comprises one or more of the following: payment of the first scheduled maintenance fee, payment of the second scheduled maintenance fee, payment of the third scheduled maintenance fee.
 40. The method of claim 37 wherein said future event comprises the filing of a litigation lawsuit involving said patent or group of patents.
 41. The method of claim 37 wherein said future event comprises the successful prosecution of a litigation lawsuit involving said patent or group of patents.
 42. The method of claim 37 wherein said selected metrics include one or more of the following: number of claims per patent, number of words per claim, different words per claim, length of patent specification, number of drawing pages or figures, number of cited prior art references, age of cited references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent, payment of maintenance fees, name of prosecuting attorney or law firm, examination art group, or length of pendency in the PTO.
 43. A method for rating a patent or group of patents, said method comprising: gathering data comprising one or more selected metrics generally identifying and/or quantifying certain selected characteristics of said patent or group of patents to be rated; providing said data as input to a mathematical rating model; using the mathematical rating model to calculate a numerical score or rating based on the data input, said mathematical rating model being statistically determined such said numerical score or rating is generally predictive of the probability of said patent or group of patents being maintained or abandoned in the future.
 44. The method of claim 43 wherein said mathematical rating model is derived by storing a first group of data comprising selected metrics identifying and/or quantifying said selected characteristics of a first population of patents for which maintenance fees have been paid, storing a second group of data comprising selected metrics identifying and/or quantifying said selected characteristics of a second population of patents for which maintenance fees have not been paid or for which it is undetermined whether maintenance fees have been paid, and using a statistical regression to create a predictive algorithm based on said stored first and second groups of data.
 45. The method of claim 43 wherein said selected metrics include one or more of the following: number of claims per patent, number of words per claim, different words per claim, length of patent specification, number of drawing pages or figures, number of cited prior art references, age of cited references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent, payment of maintenance fees, name of prosecuting attorney or law firm, examination art group, or length of pendency in the PTO.
 46. The method of claim 43 wherein said mathematical rating model comprises a multiple regression model that correlates multiple individual predictor variables comprising said selected metrics to one or more desired criterion variables comprising the corresponding output rating and/or probability of the patent or group of patents being maintained or abandoned.
 47. The method of claim 46 wherein said multiple regression model has the form: CV _(m) =ƒ{PV ₁ , PV ₂ . . . PV _(n)}where: CV_(m)=criterion variable or quality/event desired to be predicted  PV_(n)=predictor variables or selected patent metrics.
 48. The method of claim 46 wherein said regression model includes between about 15 and 25 predictor variables.
 49. The method of claim 46 comprising the further step of determining the statistical accuracy of the regression model in accordance with the general formula: SA(m)=CO/(CO+IN) where: SA(m)=statistical accuracy of regression model (m) CO=number of correct predictions for model (m) IN=number of incorrect predictions for model (m).
 50. The method of claim 49 comprising the further steps of: incrementally modifying the regression model (m) to produce a modified regression model (m+1); determining the statistical accuracy of the modified regression model (m+1); comparing the statistical accuracy of the modified regression model (m+1) to the previously determined statistical accuracy of regression model (m); and either repeating said incremental modification of the regression model (m+1) to produce a further modified regression model (m+2) if the determined statistical accuracy of the modified regression model (m+1) is greater than the determined statistical accuracy of the regression model (m), or reversing said incremental modification of regression model (m+1) to produce the original regression model (m) if the determined statistical accuracy of the modified regression model (m+1) is less than the determined statistical accuracy of the regression model (m).
 51. The method of claim 43 comprising the further step of generating a patent rating report for said selected patent or group of patents, said report including basic information identifying said selected patent or group of patents and said corresponding output rating or probability determined therefor.
 52. The method of claim 51 wherein said patent rating report is generated in response to an electronic request transmitted over a computer network and wherein said report is generated and displayed automatically without further human intervention.
 53. The method of claim 52 wherein said patent rating report contains at least one reported rating or ranking that is generally representative of the breadth (“B”) or likely infringement of the selected patent or group of patents, at least one reported rating or ranking that is generally representative of the defensibility (“D”) or likely validity of the selected patent or group of patents, and at least one reported rating or ranking that is generally representative of the commercial relevance (“R”) or technical merit of the selected patent or group of patents. 